
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "7"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 100 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 96 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.493353	-0.801943	2.623336	0.678214	0.835106	0.402623	2.486071	1.444276	0.7719	0.7698	0.2678	14.484839	13.641881
2459593	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.514833	-1.338818	2.228516	0.892343	1.394249	0.932386	2.650257	1.578294	0.7326	0.7277	0.2551	5.036800	5.262029
2459592	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.177537	-0.867183	0.513157	-0.410758	-1.160865	-0.331036	-1.817512	0.043509	0.0290	0.0315	0.0011	nan	nan
2459591	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.443150	-1.599443	3.020305	0.816832	0.559642	1.069516	0.192164	0.933606	0.6681	0.6801	0.3855	5.086945	5.574132
2459590	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.024446	-1.368388	2.470791	1.141512	0.143941	0.286186	1.980361	1.418752	0.6701	0.6790	0.3808	4.878038	5.339601
2459589	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.013048	-1.219272	1.924168	0.713870	1.034405	0.501056	2.503091	1.700863	0.6997	0.6988	0.3031	4.631576	5.183911
2459588	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.229039	-1.104457	2.987529	0.938202	3.702629	3.160871	3.325067	2.638604	0.7817	0.7639	0.2238	8.630941	7.705023
2459587	dish_ok	100.00%	-	-	-	100.00%	0.00%	351.999679	351.817865	inf	inf	103.686814	106.055724	580.207529	569.657360	nan	nan	nan	0.000000	0.000000
2459586	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.008797	-1.755008	-0.929557	-0.695887	-0.785891	0.017678	0.540941	0.557073	0.6662	0.6787	0.3738	4.024496	4.230360
2459585	dish_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.802851	-1.393619	-0.121065	-0.732402	-0.508215	-0.690709	-0.406581	1.521233	0.0320	0.0335	0.0017	0.000000	0.000000
2459584	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	16.464449	18.031567	1.174491	2.683542	2.145455	3.055114	0.081218	-0.353030	0.6223	0.6368	0.3805	3.346182	3.403052
2459583	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.368663	-2.004927	-0.728099	-0.331655	0.073881	-0.982138	0.816553	0.646337	0.6644	0.6712	0.3998	4.091368	3.987744
2459582	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.017404	-1.812071	-0.801575	-0.358988	0.617208	-0.646186	0.173450	0.373642	0.6658	0.6743	0.4045	3.934186	4.042783
2459581	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.207334	-2.189045	-0.704807	-0.205248	0.281278	-0.715348	1.044146	0.671276	0.6649	0.6755	0.4050	3.844277	3.990680
2459580	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.278771	-1.899042	-0.852208	-0.104431	-0.479677	-0.756937	1.172410	0.626970	0.6757	0.6855	0.3941	3.982806	3.989583
2459579	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.134565	-1.698998	-0.823173	-0.181453	0.153952	-0.698948	1.117608	1.410592	0.6728	0.6834	0.3969	5.054926	5.021566
2459578	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.381253	0.341023	-0.829178	-0.334704	0.154996	0.798792	0.186108	0.169799	0.0301	0.0308	0.0010	nan	nan
2459577	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.980793	-2.126525	-0.804389	-0.368642	0.519946	-0.927448	1.624914	1.515300	0.6757	0.6878	0.3938	3.764258	3.917539
2459576	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.815819	-1.648223	-0.624405	-0.627528	-0.232898	-0.507939	3.773714	2.226739	0.6787	0.6903	0.3995	2.444375	2.398498
2459575	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.664743	-1.623285	-0.766835	-0.710957	-0.746830	-1.232446	-0.619886	-0.669061	0.7330	0.7393	0.3497	0.000000	0.000000
2459574	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.768761	-1.536124	-0.725179	-0.809147	-0.079955	-0.564557	1.030902	0.099897	0.6739	0.6952	0.3871	3.204234	3.081791
2459573	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.983359	-1.524461	-0.908890	-0.032431	-0.028684	-0.991755	1.943217	1.607240	0.6651	0.6767	0.4065	0.000000	0.000000
2459572	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.283196	-1.648146	-0.796348	-0.565354	-0.184993	-0.733563	0.128520	1.035534	0.6696	0.6822	0.4078	3.084862	2.873180
2459571	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.115214	0.315524	-0.806599	-0.443043	-0.321631	-0.293866	0.088983	-0.010981	0.0294	0.0285	0.0011	nan	nan
2459570	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.667169	-1.653528	-0.744739	-0.596497	-0.774180	-0.939770	-0.694626	-0.716022	0.7397	0.7474	0.2609	3.391947	3.323712
2459566	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.727366	-1.488709	-0.635534	-0.286768	0.131270	-0.664404	-0.205650	-0.368148	0.6691	0.6806	0.4151	4.414344	3.427083
2459565	dish_ok	0.00%	-	-	-	100.00%	0.00%	-2.569066	-2.569066	-2.410329	-2.410329	0.475242	0.475242	0.005642	0.005642	nan	nan	nan	0.000000	0.000000
2459564	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.087861	1.827715	1.498464	1.791613	-0.533368	-0.644579	-1.585287	-0.905045	0.6887	0.6906	0.3925	3.102455	2.897288
2459563	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.811741	5.615050	20.906049	21.692442	9.988611	12.823510	-1.275346	-0.784249	0.6635	0.6642	0.4074	3.626837	3.575106
2459562	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.334726	0.908766	0.283750	0.406305	-1.165033	-1.002704	-1.254311	-1.201232	0.6754	0.6787	0.4005	3.230003	2.841992
2459561	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.780272	1.464300	0.778120	0.962635	-0.984448	-0.668229	-1.853999	-1.906954	0.6760	0.6809	0.3945	2.962656	2.715591
2459560	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.353097	1.148831	0.387991	0.770186	-0.716993	-0.230961	-1.406244	-1.492318	0.6704	0.6732	0.4095	2.739097	2.362503
2459559	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.891666	1.788080	0.966101	1.259619	-0.373198	0.111052	-1.367066	-1.416848	0.6838	0.6919	0.3920	2.821652	2.689530
2459558	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.547919	5.678000	11.222965	11.234413	10.769939	12.577074	-1.580670	-1.632112	0.6845	0.6852	0.3978	2.929775	2.717929
2459557	dish_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0327	0.0328	0.0020	nan	nan
2459556	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.067891	6.122563	11.760552	11.623913	10.331830	11.543308	-1.073957	-2.322464	0.6931	0.6929	0.3869	3.190900	2.948700
2459554	dish_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6798	0.6806	0.4046	nan	nan
2459553	dish_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6868	0.6874	0.4006	nan	nan
2459552	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.026214	6.770086	15.394187	16.260110	16.000694	18.314996	0.451497	0.426383	0.6892	0.6848	0.3995	0.000000	0.000000
2459551	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.638302	1.344756	0.710753	1.045964	-1.243701	-0.284466	-1.552622	-1.680402	0.6806	0.6809	0.3954	2.985499	2.813915
2459550	dish_ok	-	98.35%	98.35%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0536	0.0525	0.0004	nan	nan
2459549	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.603556	5.512312	11.286712	11.898597	10.543131	12.823884	-1.585351	-1.913103	0.6834	0.6833	0.4061	2.883217	2.841764
2459542	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.947055	5.645491	22.480339	23.842411	0.303752	1.011508	-1.682497	-1.701931	0.7447	0.7337	0.3704	2.786912	2.684151
2459541	dish_ok	100.00%	1.79%	1.79%	0.00%	100.00%	0.00%	4.655512	4.721479	14.140159	14.074868	8.865609	10.976197	3.770148	3.611408	0.7308	0.7166	0.3431	3.551974	3.514038
2459540	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.727425	2.187833	2.141743	2.189453	0.313075	0.580489	-1.075569	-0.808440	0.7395	0.7189	0.3683	3.284629	2.979048
2459536	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.937605	8.913253	18.785404	19.831688	17.198630	19.018944	-2.412593	-3.627811	0.7203	0.7223	0.4194	4.484469	4.263835
2459535	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.149494	1.510624	1.640066	1.891661	-0.145932	0.889837	-1.242154	-1.347206	0.7731	0.7428	0.3878	2.795794	2.735019
2459534	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.320673	1.133439	0.675841	1.004310	-0.607882	-0.097953	-0.841865	-1.313463	0.7654	0.7462	0.3721	3.701529	3.366640
2459533	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.971194	6.057153	11.640656	12.049802	9.636580	11.492789	-1.782352	-1.658080	0.7091	0.7064	0.4058	3.000780	2.958113
2459532	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.992017	7.123660	15.304028	15.446056	11.691361	14.249984	-1.589267	-1.844122	0.7086	0.7028	0.4059	3.429477	3.434825
2459530	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.411992	1.349664	0.637735	0.866751	-0.964981	-0.317579	-1.507453	-1.343367	0.7308	0.7203	0.4059	3.039947	3.003674
2459527	dish_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	dish_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	dish_ok	100.00%	100.00%	100.00%	0.00%	-	-	4.644878	4.570170	6.289623	7.389406	4.385400	2.678515	2.073803	1.071224	0.0319	0.0346	0.0017	nan	nan
2459508	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.292764	7.244420	9.736658	9.811095	10.355695	8.970741	8.647749	10.735728	0.8733	0.8950	0.2718	0.000000	0.000000
2459505	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.412401	4.186345	12.700752	11.556608	3.455093	5.790702	-0.864102	-0.676536	0.8779	0.8350	0.2279	7.368158	7.176811

Antenna Season Report Notebook¶

Antenna 7 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 7, Node 2:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 7: 2459624

Antenna 7: 2459623

Antenna 7: 2459622

Antenna 7: 2459621

Antenna 7: 2459620

Antenna 7: 2459619

Antenna 7: 2459618

Antenna 7: 2459617

Antenna 7: 2459616

Antenna 7: 2459615

Antenna 7: 2459614

Antenna 7: 2459613

Antenna 7: 2459612

Antenna 7: 2459611

Antenna 7: 2459610

Antenna 7: 2459609

Antenna 7: 2459608

Antenna 7: 2459607

Antenna 7: 2459605

Antenna 7: 2459604

Antenna 7: 2459603

Antenna 7: 2459602

Antenna 7: 2459598

Antenna 7: 2459597

Antenna 7: 2459596

Antenna 7: 2459595

Antenna 7: 2459594

Antenna 7: 2459593

Antenna 7: 2459592

Antenna 7: 2459591

Antenna 7: 2459590

Antenna 7: 2459589

Antenna 7: 2459588

Antenna 7: 2459587

Antenna 7: 2459586

Antenna 7: 2459585

Antenna 7: 2459584

Antenna 7: 2459583

Antenna 7: 2459582

Antenna 7: 2459581

Antenna 7: 2459580

Antenna 7: 2459579

Antenna 7: 2459578

Antenna 7: 2459577

Antenna 7: 2459576

Antenna 7: 2459575

Antenna 7: 2459574

Antenna 7: 2459573

Antenna 7: 2459572

Antenna 7: 2459571

Antenna 7: 2459570

Antenna 7: 2459569

Antenna 7: 2459566

Antenna 7: 2459565

Antenna 7: 2459564

Antenna 7: 2459563

Antenna 7: 2459562

Antenna 7: 2459561

Antenna 7: 2459560

Antenna 7: 2459559

Antenna 7: 2459558

Antenna 7: 2459556

Antenna 7: 2459552

Antenna 7: 2459551

Antenna 7: 2459549

Antenna 7: 2459542

Antenna 7: 2459541

Antenna 7: 2459540

Antenna 7: 2459536

Antenna 7: 2459535

Antenna 7: 2459534

Antenna 7: 2459533

Antenna 7: 2459532

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶